With the ongoing West African Ebola epidemic projected to get even worse, relief organizations are struggling to obtain data of high enough quality to reign the outbreak in. Many interventions rely on epidemiological models of the disease’s spread, but with poor or non-existent record-keeping in many of the rural villages affected by the outbreak, such models have trouble accurately predicting future conditions. One approach is the U.S. Centers for Disease Control’s Epi Info VHF project, a software tool to help rapidly identify people who have been exposed to the disease. Other efforts, such as the nonprofit HealthMap, are using social media data to try to map the spread of the disease in areas where official health data is lacking.

The Department of Homeland Security (DHS) wants to use the Internet of Things to help improve the nation’s disaster communications systems. The department is working with the Pacific Northwest National Laboratory, which recently submitted a request for information on Internet-connected equipment for first responders, such as oxygen tanks for firefighters that relay oxygen levels to a remote database. DHS will soon begin experimenting with example solutions from the private sector.

Google has released a set of software tools called CausalImpact to help infer causal relationships from correlations in data. The tools, which are designed for the R statistical computing language, were originally designed to measure the impact of the company’s AdWords campaigns, but the creators hope they can be applied in domains outside of advertising. In many fields, particularly social science domains where controlled experiments are impractical, it can be difficult to establish causal links between changes to products or policies and effects in a population. Although CausalImpact cannot always establish causality with certainty, it can offer data scientists a probability that a given correlation might contain a causal link.

In the future, traffic lights will help cut congestion in cities by not just reacting to the flow of traffic, but also predicting how people will drive in response to changes in traffic and signals. In Utah, the state’s Department of Transportation has already implemented some of these features and can adjust the timing of nearly any signal in the state within 30 seconds. Part of the reason the state’s system is so good is that all the traffic signals and closed-circuit cameras that detect traffic are connected to a statewide fiber-optic network. Another project at Carnegie Mellon University in Pittsburgh seeks to create smarter traffic signals without the costly investment of statewide control infrastructure. That project uses radar sensors to measure traffic flow and adjust nearby traffic signals almost instantly.

Multinational food and commodity company Cargill has begun marketing data analysis software that helps farmers maximize their outputs. The software, called NextField DataRx, draws from a massive database of different kinds of seeds’ performance in different soil and environmental conditions, and recommends which seeds to plant, what pesticides to use, and other strategies for maximizing yields. Cargill estimates that the software can help corn farmers increase their yields by five to 10 percent per acre. The product competes with similar offerings from Monsanto and DuPont.

Researchers from North Carolina State University have shown that deep learning can improve video game artificial intelligence. Deep learning, a branch of machine learning inspired by the function of neurons in the brain, can help create predictive models that are more complex than those available to traditional machine learning methods. The researchers used previous gameplay data to predict with over 60 percent accuracy what goals a player was trying to achieve, which could enable a game to increase obstacles associated with those specific goals. This represents a nearly 30 percent improvement over existing methods for predicting players’ goals.

The City of Austin, Texas is ramping up its data analytics efforts. For the past several years, the city has been using an internal data discovery system to connect data sources from different city departments, and now it has invested in an ambitious business analytics component that will make analyzing and acting on insights from interdepartmental data much easier. The city will work with analytics company MicroStrategy to develop the system, which will connect crime, finance, maintenance, and permitting data, among other sources.

Chicago’s Lincoln Park Zoo is launching a citizen science initiative to help create a database of urban wildlife. The project will let citizens view over 1 million photographs of animals captured by cameras throughout the Chicago area and classify their species. The area is home to a wide range of animals, including foxes, rabbits, woodchucks, and beavers, which makes the job difficult to automate. The goal of the project is to assess the city’s biodiversity to provide policymakers and conservationists with data to help inform decisions around controlling the wildlife.

Music streaming services have collected an enormous amount of information on music and listener behavior, and streaming service Spotify has launched a blog called Spotify Insights to detail some of the lessons it has learned. With the help of Echo Nest, a music data analysis company Spotify recently acquired, the blog will draw from complex machine learning analysis to visualize and map information on its users habits. The blog’s first post features the migration of music genres from their countries of origin to other places around the world.

Zetta, an open source project to provide tools for building Internet of Things (IoT) devices, launched this week. The creators of the project, from data management company Apigee, hope Zetta will help developers build IoT devices that can communicate with one another through a common set of protocols. Zetta joins other IoT interoperability initiatives from industry groups such as the Industrial Internet Consortium and the HyperCat consortium.